The Trillion-Dollar R&D Fix

View more from the

Executive Summary

Reprint: R1205D

How does a company know what kind of return it’s getting from R&D? Is it better at R&D than the competition? How much should it be spending and what can it do to improve the effectiveness of those investments? Existing measures of R&D effectiveness don’t answer any of those questions.

In this article Anne Marie Knott, a professor at Washington University’s Olin School of Management, presents a new metric for R&D productivity: RQ, short for research quotient. It allows managers not only to estimate the effectiveness of R&D investment relative to the competition but also to see how changes in R&D expenditure affect the bottom line and, most important, a company’s market value.

According to her research, if the top 20 firms traded on U.S. exchanges had increased their 2010 R&D investment using the RQ method, the collective increase in market cap would have been a whopping $1 trillion. The longer-run benefits are potentially even greater because the measure allows companies to more closely link changes in R&D strategy, practices, and processes to profitability and value.

This is a story that has played out before: Thirty years ago, W. Edwards Deming’s quality metrics inspired the TQM movement, which revolutionized the way companies manufacture. RQ can do the same for R&D, the author believes—and the payoff will be even greater.

Artwork: Ricky Allman, Fluid Redux, 2010, acrylic on panel, 36″ x 48″

How does a company know what kind of return it’s getting from R&D? Is it better at R&D than the competition? How much should it be spending, and what can it do to improve the effectiveness of its investments?

Existing measures of R&D effectiveness—for instance, amount of spending or number of patents—don’t answer those questions or reliably predict market value. Year after year Booz & Company publishes “The Global Innovation 1000.” And year after year the consulting giant points out that R&D spending does not correlate with market value or growth. The 2010 report argues, “Spending more on R&D won’t drive results. The most crucial factors are strategic alignment and a culture that supports innovation.” The trouble is that it’s also hard to measure strategic alignment and culture, let alone link them to profitability or market value.

R&D is thus an easy target when firms face quarterly earnings pressure. Since it is expensed rather than capitalized, cuts yield immediate increases in profit, while the detrimental impact of those cuts aren’t felt for a few years. In marginal trade-offs between investments in, say, physical capital or advertising, whose returns are more quantifiable, R&D loses out. In response to the recent prolonged recession, for instance, firms with revenues greater than $100 million reduced their R&D intensity (R&D spend divided by revenue) by 5.6%, on average, whereas capital intensity at those firms fell only 4.8% and advertising intensity actually increased 3.4%.

A new metric for R&D productivity—which I call RQ, short for research quotient—can change all that. RQ allows you to estimate the effectiveness of your R&D investment relative to the competition and to see how changes in your R&D expenditure affect the bottom line and, most important, your company’s market value. My research—which includes a comprehensive analysis of all publicly traded companies in the U.S.—suggests that if the top 20 firms traded on U.S. exchanges had optimized their 2010 R&D spending using the RQ method, the collective increase in market cap would have been an astonishing $1 trillion. The longer-term benefits are even greater, as RQ also allows companies to link changes in R&D strategy, practices, and processes more closely to profitability and value.

This is a story we’ve seen played out before: Thirty years ago, W. Edwards Deming’s quality metrics inspired the TQM movement, which revolutionized the way companies manufacture. I believe that RQ can do the same for R&D—and that the payoff will be even greater.

The Measure

Calculating RQ doesn’t involve fancy new math. Economists have been calculating capital and labor productivity for years—that is, determining the marginal value of increasing either one. R&D productivity can be determined using the same method, although few, if any, analysts or academics have done so at the level of individual companies.

Essentially, the equation defines the relationship between a firm’s inputs (what it spends) and its output (its revenues). The formula typically considers two costs, capital and labor. Of course, those aren’t the only determinants of revenue, and most economists would accept that the equation could be expanded to include another central input: R&D. Using standard regression analysis, the calculation tells us in a very precise way how productive each of the inputs is in generating output. It tells us, for instance, how much a 1% increase in R&D spending would increase a firm’s revenue.

A precise estimation of RQ examines thousands of firms simultaneously using fairly sophisticated software, but a coarse estimate of a single firm’s RQ can be run on an ordinary spreadsheet using historical data easily obtainable at most large companies—revenue figures and spending on PP&E (property, plant, and equipment), employment, and research.

See the sidebar “The Theory Behind RQ” for more on the method:

The Theory Behind RQ

Definition

The RQ method for measuring productivity of R&D investment uses the well-known economic formula for measuring labor and capital productivity. This equation defines the relationship between a firm’s inputs (what it spends) and output (its revenues). The formula typically looks like this:

The exponents indicate how productive each input is in generating output. Specifically, they show the percentage increase in a firm’s revenues resulting from a 1% increase in capital (alpha) or labor (beta). The RQ method expands the calculation to include another input, R&D (R):

The new exponent, gamma, tells you how much of a percentage increase in output you would get from a 1% increase in r&d spending.

Calculation

To calculate your RQ, you need several years’ data on revenues and annual expenditures on PP&E (property, plant, and equipment), labor, and R&D. Those data are converted into logs, a standard transformation needed to run the regression analysis that produces RQ. The spreadsheet looks something like this:

When you run the regression analysis, you get productivity levels for PP&E, employees, and R&D. An analysis of the full set of U.S. publicly traded companies yielded an average R&D exponent of 0.109—which means that increasing R&D spending by 1% would increase revenue by 0.11%.

Comparison

Finally, to compare research productivity across firms and to help firms track changes in their R&D productivity, you rescale the exponent number relative to the mean of all U.S. traded firms to create an index number. (An RQ of 100, therefore, denotes the average R&D exponent across all firms.) Most companies (67%) have RQs between 85 and 115.

Once you know your RQ—how effective your company is at R&D—you can determine the amount of R&D spending that would produce the maximum profits. That calculation involves a standard piece of math, called a partial derivative, that can be easily embedded in a spreadsheet. In essence, it’s an exercise in marginal returns—determining at what point an additional dollar spent on R&D begins to reduce revenues and profitability.

Why It Works

Good measures have three properties: universality, uniformity, and reliability. Uniformity means the measure is interpreted the same way in all contexts; universality means it applies to all relevant entities (in this case, firms); and reliability means that its predictions confirm what theory says should happen. The easiest way to explain why these properties are important is to show why another measure often used to gauge R&D effectiveness—patent counts—fails because it lacks them.

First, patent counts aren’t universal in that not all firms doing R&D patent their innovations. In fact, fewer than 50% of firms engaged in R&D file patents in any given year. Moreover, even among patenting firms, few of them patent all their innovations. It’s often more effective to protect intellectual property by keeping it a trade secret. Patents aren’t uniform, either. Compare, for example, the economic value of the patent for copying DNA with that of the 97% of patents that are never commercialized. On average, 10% of patents account for up to 85% of the value of all patents. Finally, higher patent counts don’t reliably predict higher profits and market value—the outcome companies expect from R&D investments.

In contrast, RQ exhibits all three properties. RQ is estimated entirely from standard financial data, so it can be calculated for any firm doing R&D. And because RQ is a ratio, its interpretation is uniform across firms regardless of currency. Most important, RQ is reliable. It confirms what you would expect it to: (1) that firms with higher RQ—those that are better at R&D—spend more on R&D than firms with low RQ; (2) that R&D spending beyond the optimal limit identified by RQ reduces firm market value; and (3) that firms with higher RQ have higher profits and market value for a given set of inputs.

My colleagues Carl Vieregger and James Yen and I have demonstrated all three effects rigorously across all publicly traded U.S. firms from 1981 through 2006. Our analysis of the data shows that a 10% increase in RQ—that is, in R&D productivity—results in an increase in market value of 1.1%.

The Payoff

Using the RQ measure has immediate benefits. Firms—not to mention the financial analysts who track them—can now identify the marginal returns to R&D and the level of R&D investment that generates the greatest market cap. As the exhibit “The Trillion-Dollar Opportunity” illustrates, the gains from bringing R&D closer in line with optimal levels prescribed by RQ are enormous.

The Trillion-Dollar Opportunity

My research suggests that if the top 20 firms traded on U.S. exchanges had optimized their R&D spending in 2010 using the RQ method, the collective increase in market cap would have been an astonishing $1 trillion.

For most companies, RQ will call for significant increases to R&D budgets. A few firms, of course, will find the opposite. To reach its optimal level, Pfizer would have to cut its R&D spending by $3 billion a year, money that would be freed up for investment in other, more productive things. Once companies adopt RQ as a standard metric and align their spending accordingly, market caps should rise very quickly since increases to market value typically materialize as soon as beliefs about future performance change.

Firms can identify the marginal returns to R&D and the level of investment that generates the greatest market cap.

Over the long term, RQ will improve the quality and effectiveness of R&D initiatives. Managers will be able to determine, for instance, whether a given change in R&D strategy translates over time into a higher or lower RQ. And as managers and analysts get better at measuring the success of initiatives, they will be able to make better judgments about the quality of firms’ management decisions and start to understand which R&D practices create the most value in various contexts.

Since the measure is new and not widely used, I can’t make conclusive statements about which practices and processes improve RQ. However, a National Science Foundation study I conducted with Bruno Cassiman, of IESE Business School, suggests three preliminary insights:

RQ rises with the breadth of a firm’s activity.

RQ is positively correlated with the number of markets a company sells to outside its home region (export breadth), the number of locations for R&D activity (technical breadth), and the number of product lines (product breadth).

In-house research trumps outsourced R&D.

RQ is negatively correlated with cooperative R&D, and the correlation between RQ and R&D is higher for internal R&D than external R&D.

RQ varies for different types of innovation.

RQ is positively correlated with product (versus process) innovation. Also, RQ is higher for companies that do incremental innovation (those that are new to the firm) rather than radical innovation (those that are new to the world). And RQ is positively correlated with organizational innovations that complement product innovations.

For most companies, RQ will call for significant increases to R&D budgets. A few firms will find the opposite.

These insights, while useful, don’t reveal why or how they act to improve R&D productivity. Nonetheless, managers—and analysts—who use RQ will be able to tell quite a lot about what’s likely to happen to any company’s share price in response to changes in strategy and management practice. The case of Trimble Navigation provides an interesting example.

The company was founded in 1978 to develop positioning and navigation products utilizing LORAN (and subsequently GPS) technology. While Trimble’s initial markets were in military applications for which the technology was originally developed, it quickly applied the technology to commercial markets, such as surveying and mariner navigation.

Trimble’s net income grew fairly rapidly from 2000 to 2007 but then suffered a steep decline in 2009, largely owing to the recession. Trimble’s profit collapse of 54% was on par with the 62% average decline for U.S. publicly traded firms. But Trimble failed to bounce back. The average net income for U.S. firms is now 8.5% higher than the pre-recession peak, whereas Trimble’s net income remains 27% below its peak.

Trimble’s RQ history reveals what may account for this prolonged slump. Trimble’s RQ steadily increased though 2004—the peak in RQ preceded the peak in market cap by about three years and increases in net income by about four years. In 2004, however, Trimble’s RQ fell 40%. Three years later, market cap plunged; the following year, profitability took a hit.

The drop in RQ can be linked to changes in Trimble’s strategy. Throughout the 1990s Trimble developed and patented many technologies, reaching a peak of 94 patents in 1997. In addition, Trimble was rapidly expanding the product markets in which this technology was deployed, according to the “Company History” on the firm’s website. In 2000, however, the company appears to have changed its strategy from one of in-house development to acquisition. This switch is documented in the “Company History” (up until 2000, each year’s summary described a technological development; after 2000 there is no mention of developments, only of acquisitions) and by the decline in patents obtained per year, which dropped to almost zero. A shrewd analyst might pick up on either or both of these patterns, but without RQ it would be difficult to tell whether the change in strategy was value-enhancing or value-destroying. Using RQ, a manager or analyst could easily see that the shift was value-destroying.

The Promise

At first, RQ might be viewed with suspicion, even hostility, at many corporations, appearing to be a device the R&D community could use to line its coffers. But concern over providing yet another metric for company managers to abuse should not outweigh the substantial long-term benefits that RQ can deliver.

Improving manufacturing operations creates value, but R&D is a basic engine of economic and social growth. If enough firms adopt RQ and align their R&D spending and strategies accordingly, we should see a systematic improvement in overall corporate R&D effectiveness. The benefits for us all would be remarkable.

A version of this article appeared in the May 2012 issue of Harvard Business Review.